Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain

doi:10.1016/j.infrared.2018.04.017

CIOMP OpenIR

	Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain
	Cheng, B. Y.; Jin, L. X.; Li, G. N.
	2018
发表期刊	Infrared Physics & Technology
ISSN	1350-4495
卷号	92 页码:30-43
摘要	This paper presents an adaptive fusion framework of infrared and visual images using saliency detection and an improved dual-channel pulse-coupled neural network (ID-PCNN) in the local non-subsampled shearlet transform (LNSST) domain. The first step is to use the LNSST, an upgrade of the non-subsampled shearlet transform, for multi-scale analysis to separate the source images into low-pass and high-pass sub-images. The final fusion effect is determined by the fusion rule of the low-pass component. Thus, an improved algorithm based on frequency-tuned saliency extraction is adopted to guide the adaptive weighted fusion of the low-pass sub-image. An ID-PCNN model is used as the fusion rule for high-pass sub-images. A sum of directional gradients acts as the linking strength to characterize the texture details of an image. A modified spatial frequency that reflects the gradient features of images is used to motivate neurons. A series of images from diverse scenes is used for fusion experiments. Fusion results are evaluated subjectively and objectively. The results show that our algorithm exhibits superior fusion performance and is more effective than typical fusion techniques. (C) 2018 Elsevier B.V. All rights reserved.
关键词	LNSST Image fusion Improved dual-channel PCNN Frequency-tuned saliency detection sparse representation contourlet transform shearlet transform feature-extraction algorithm decomposition Instruments & Instrumentation Optics Physics
DOI	10.1016/j.infrared.2018.04.017
收录类别	SCI ; EI
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/60891
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Cheng, B. Y.,Jin, L. X.,Li, G. N.. Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain[J]. Infrared Physics & Technology,2018,92:30-43.
APA	Cheng, B. Y.,Jin, L. X.,&Li, G. N..(2018).Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain.Infrared Physics & Technology,92,30-43.
MLA	Cheng, B. Y.,et al."Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain".Infrared Physics & Technology 92(2018):30-43.

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